Explanation:
AWS Glue DataBrew provides a no-code visual interface for data transformation. Creating a recipe with the COUNT_DISTINCT function on the concatenated name column requires no code, no cluster setup, and no crawler/catalog configuration. DataBrew can read the XLS file directly from S3 and execute the recipe with minimal effort — the least operationally intensive option for this task.
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Question 38. A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size. A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file. Which solution will meet this requirement with the LEAST operational effort?
A
Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.
B
Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
C
Create and run an Apache Spark job in an Amazon EMR Serverless to calculate the number of distinct customers.
D
Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.
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